Stop Using AI. Start Hiring It.

Most of us still "use" AI. Open a chat, type a prompt, get an answer, close the tab. The author argues that era is ending. AI agents have become productive enough that getting work out of them is no longer the hard part. The hard part is finding enough human attention to check what they have done. Once you see that clearly, you stop treating AI as a tool you pick up and start treating it as someone you hire.

The lesson starts with giving every agent its own computer. One team tried running five agents in the same code checkout, and the results were chaotic: one agent stashed everyone else's work, and another wiped the checkout entirely. Their fix was to give each agent its own virtual desktop, an isolated container running a full Linux desktop with its own file system, browser, terminal and code editor. Agents can see what they build, so front-end work can be tested in a real browser. Because the desktops live on shared servers, a developer in Tokyo can hand off an agent to a developer in London exactly where it left off.

The team began by building an on-premise alternative to OpenAI, but customers kept asking what they should actually do with it. The answer was mostly coding, and AI coding tools were not built for teams. So they built a kanban board where agents do the work. Each card is a task about the size of a user story. Agents read the codebase and write a spec, a human reviews it, the agent builds what was approved, and the work passes code review before merging.

The approach has limits, and the author is candid that it can fall apart. Still, the core shift is clear: the bottleneck is human review, so the job becomes managing and checking work rather than prompting for answers.

我们大多数人仍然在“使用”AI。打开聊天窗口，输入提示词，得到答案，然后关闭标签页。作者认为，这个时代正在结束。AI智能体的生产力已经足够高，让它们产出工作不再是难点。真正的难点在于，要找到足够的人力注意力去检查它们做了什么。一旦你清楚地看到这一点，你就不会再把AI当作随手拿起的工具，而会把它当作你要雇用的人。

这一切的起点，是为每个智能体配备它自己的“电脑”。有一个团队尝试让五个智能体在同一个代码检出目录中同时工作，结果一片混乱：一个智能体把其他人的工作全部暂存起来，另一个则彻底清空了整个检出目录。他们的解决方案是为每个智能体分配独立的虚拟桌面，即一个隔离的容器，运行完整的Linux桌面，拥有自己的文件系统、浏览器、终端和代码编辑器。智能体可以看到它们构建的内容，因此前端工作可以在真实的浏览器中进行测试。由于这些桌面运行在共享服务器上，东京的开发者可以把一个智能体交接给伦敦的开发者，对方能从原来的进度无缝接续。

该团队最初是在构建一个可部署在本地的OpenAI替代方案，但客户不断询问实际上该用它做什么。答案主要是编程，而当时的AI编程工具并非为团队协作而设计。于是，他们构建了一个看板，由智能体在上面完成工作。每张卡片对应一个大小约相当于用户故事的任务。智能体阅读代码库并撰写规格说明，由人工审阅，智能体再按批准的内容进行构建，工作成果在合并前还需通过代码审查。

作者坦言，这种方法有其局限，也可能失效。但核心转变是清晰的：瓶颈在于人工审阅，因此工作重心从向AI提问，转向管理和检查工作成果。